In order to achieve a state-of-charge (SOC) balance among multiple energy storage units (MESUs) in an islanded DC microgrid, a SOC balancing and coordinated control strategy based on the adaptive droop coefficient algorithm for MESUs is proposed. When the SOC deviation is significant, the droop coefficient for an energy storage unit (ESU) with a higher (or lower) SOC is set to a minimum value when discharging (or charging). The ESU with the higher (or lower) SOC is controlled to discharge (or charge) with the rated power, while the other ESU compensates for the remaining power when the demanded discharging (or charging) power is greater than the rated power of the individual ESU. Otherwise, when the demanded discharging (or charging) power is lower than the rated power of either ESU, the ESU with the higher (or lower) SOC releases (or absorbs) almost all the required power while the other ESU barely absorbs or releases power, thus quickly realizing SOC balancing. When the SOC deviation is slight, the fuzzy logic algorithm dynamically adjusts the droop coefficient and changes the power distribution relationship to balance the SOC accurately. Furthermore, a bus voltage recovery control scheme is employed to regulate the bus voltage, thus improving the voltage quality. The energy coordinated management strategy is adopted to ensure the power balance and stabilize the bus voltage in the DC microgrid. A simulation model is built in MATLAB/Simulink, and the simulation results demonstrate the effectiveness of the proposed control strategy in achieving fast and accurate SOC balance and regulating the bus voltage.

Predicting the remaining useful life (RUL) of lithium-ion batteries is crucial for enhancing their reliability and safety. Addressing the issue of inaccurate RUL predictions caused by the nonlinear decay resulting from capacity regeneration, this paper proposes an indirect lithium-ion battery RUL prediction method based on complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN) and convolutional neural network (CNN)–bidirectional gated recurrent unit (BiGRU). The method extracts Health Indicators (HI) from the battery-charging stage and employs CEEMDAN to decompose HI into several components. These components are then input into a component prediction model for forecasting. Finally, the predicted component results are fused and input into a capacity prediction model to achieve indirect RUL prediction. Validation is conducted using the lithium-ion battery dataset provided by NASA. The results indicate that, under prediction starting points (STs) of 80 and 100, the maximum average absolute errors do not exceed 0.0096 and 0.0081, and the maximum root mean square errors do not exceed 0.0196 and 0.0115, demonstrating high precision and reliability.

This paper proposes a novel virtual inertia control (VIC) method based on a feedforward decoupling strategy to address the low inertia issue of power-converter-interfaced microgrids. The feedforward control scheme is employed to eliminate the coupling between active and reactive power caused by line impedance. The active power-voltage droop can be applied to the battery converter in the hybrid energy storage system (HESS). A novel VIC method is developed for the supercapacitor (SC) converter of HESS to increase the inertia of the microgrid. Detailed small-signal modeling of the SC converter with the proposed VIC was conducted, and the transfer function model was obtained. Parameter analysis of the virtual inertia and virtual damping was carried out with the pole-zero map method, and the step response characteristic of output voltage amplitude with power variation was analyzed in detail, deriving the parameter design principle. The simulation study verifies the effectiveness and validity of the proposed control strategy. The proposed feedforward decoupling method and VIC can be widely applied in microgrids to enhance inertia and improve their power quality.

Matrix converters (MC) have been considered for applications in renewable power generation and microgrids. However, similar to other power electronic converter-interfaced power generation systems, the MC-interfaced system cannot provide inertia or damping effect to support the grid’s frequency or voltage. To improve the inertia and damping effect of the MC-interfaced distributed generation system and realize the grid-friendly connection of new energy power generation, a virtual synchronous generator (VSG) control strategy suitable for the MC-interfaced system is proposed. Aiming at the problem that the system frequency in the traditional VSG cannot be restored to the nominal value after the load power fluctuations, the VSG secondary frequency control strategy is introduced. To ensure a smooth grid connection, a synchronization control strategy based on virtual power has been designed. An experimental validation platform was built and the experimental results verify the correctness and effectiveness of the proposed control strategies for a grid-forming MC.